This International Feedstocks data portal supports the Global Biomass Resource Assessment,a multi-country government-led initiative dedicated to advancing the global transition to a sustainable bio-based economy. This product shares data assembled from citable sources around the globe, as reported for current biomass production as well as potential additional future production in some cases. Data were compiled into consistent classes based on the most recent reports received (ranging from 2018 to 2024).
The results from this new global sustainable supply assessment will allow scientists, policymakers, and industry leaders to explore potential sources of biomass as a foundation for a circular and sustainable global bioeconomy, supporting clean fuels, chemicals, materials and other products. The assessment was conducted by researchers at the U.S. Department of Energy (DOE) Oak Ridge National Laboratory (ORNL), with funding provided by the U.S. Department of State, and managed through DOE’s Bioenergy Technologies Office (BETO), on behalf of the CEM Biofuture Initiative and Mission Innovation. This data includes biomass resources available in many developing economies which often do not have fully advanced biomass industries. The assessment also aims to address the need for internationally accepted benchmarks quantifying sustainable biomass feedstock supplies that can be available to support a growing, circular and climate-smart bioeconomy.
The link below provides access to the data which can be filtered by country of interest and resource, as well as timeframe for the available biomass. The data are being shared based on the information received to date (references to sources are noted for each reported nation). We aim to improve and update this preliminary version of the data set in the future, based on user feedback. Please send suggestions for improvement and references to additional sources of data, or corrections to the reported data. [Data comments can be sent to biomass.updates@ornl.gov]
This data can be filtered by country and downloaded for further analysis. For example, the country of Uruguay is summarized below for available resources by year of production.
This dataset was compiled through an inventory and review of current sources of data and biomass resource assessments, including recent (since 2018) national, regional, and global assessments. See individual citations within the dataset.
This dataset contains data on forest production. The forestry products in this dataset includes hardwood, softwood, and mixed, and the dataset was obtained from the database of the 2023 Billion-Ton Report (Davis et al., 2024). The intended use is for the Feedstock Production Emissions to Air Model (FPEAM).
If you would also like access to this dataset, please use the "contact" button for a request to our research staff.
material_class: This indicates the label to denote land source or analysis source corresponding to the forest land chapter of the 2023 Billion-Ton Report.
subclass: This indicates the class of biomass.
resource_name: This indicates the type of resource.
diacls: This indicates the class of tree diameter. Class 1 has a diameter at breast height (DBH) that is greater than 11-inch, Class 2 has a DBH that is between 5- and 11-inch, and Class 3 has a DBH that is less than 5-inch.
owner: This indicates the owner such as public, private, and null or unknown.
fips: This is a geographically defined variable corresponding to counties provided as a 5-digit code.
county: This indicates the name of the county.
state: This indicates the name of the state.
County square Miles: This indicates the total area of the county in square miles including water.
scenario_name: This indicates the name of the scenario defined in the 2023 Billion-Ton Report. “mm” indicates mature market scenario, and it also contains “low”, “high”, and “emerging” scenarios.
scenario_price_offered: This indicates a price offered for a ton of biomass.
production: This indicates the total production of the forestry biomass in dry ton.
production_unit: This indicates the unit of production.
production_energy_content: This indicates the total energy contents in BTU which is production tons multiplied by BTU/ton.
energy_content_unit: This indicates the unit of energy content.
production_density_dtpersqmi: This indicates a production divided by the total area of the county.
harvest_costs: This indicates the cost of harvesting.
harvest_area: This indicates the total harvested area in acres.
harv_area_unit: This indicates the unit of harvested area.
lipid_based: This indicates either the biomass is based on lipid or not.
ash_percentage: This indicates the content of ash in percentage.
This dataset contains harvesting, chipping, and production cost data for forestland production by region and forest harvest system. The dataset supports Biomass from the forested land base analysis in the BT23 (Davis et al., 2024) and subsequent modeling using the Forest Sustainable and Economic Analysis Model (ForSEAM). The cost data was updated by Burton English and is in 2014 dollars and 2021 dollars. Data sources can be found in the accompanying PDF, ‘2021 Biomass Production Costs for the 2024 Billion Ton Analysis’, and the details can be found in the accompanying Microsoft Word file.
Cost_by_FHS_and_Region.csv is a comma-separated file that holds the following variables: Forest_Harvest_Systems: This is for different forest silvicultural and harvest methods.
Region: This is for different production cost zones of the CONUS as defined by ForSEAM.
Slope_over_40%: This is a binary variable to indicate if the slope exceeds 40%.
CutToLength: This is a binary variable to indicate if the cut-to-length method is used.
Cut: This indicates the forest management practice of either thinning or clearcutting.
Type: This indicates the type of forest.
Harverst_Production_Costs_2021$_per_bdt: The harvest costs of production per bone dry ton (bdt) in 2021 dollars.
Chipping_Costs_2021$_per_bdt: The costs of production to chip the biomass per bone dry ton (bdt) in 2021 dollars.
Total_Costs_2021$_per_bdt: The costs of production to harvest and chip the biomass per bone dry ton (bdt) in 2021 dollars.
Harverst_Production_Costs_2014$_per_bdt: The harvest costs of production per bone dry ton (bdt) in 2014 dollars.
Chipping_Costs_2014$_per_bdt: The costs of production to chip the biomass per bone dry ton (bdt) in 2014 dollars.
Total_Costs_2014$_per_bdt: The costs of production to harvest and chip the biomass per bone dry ton (bdt) in 2014 dollars.
Total_Forest_Production_Costs_2021$_per_bdt: The total production costs of forestland biomass per bone dry ton (bdt) in 2021 dollars.
Timber_Costs_2014$_per_dry_ton: The costs for timber per dry ton biomass in 2014 dollars.
Chipper_Costs_2014$_per_dry_ton: The costs for chipper per dry ton biomass in 2014 dollars.
Total_Costs_2014$_per_dry_ton: The costs for timber and chipper per dry ton biomass in 2014 dollars.
Cost_by_Region.csv is a comma-separated file that holds the following variables:
Region: This is for different production cost zones of the CONUS as defined by ForSEAM.
Slope_over_40%: This is a binary variable to indicate if the slope exceeds 40%.
CutToLength: This is a binary variable to indicate if the cut-to-length method is used.
Type: This indicates the type of forest.
Cut: This indicates the forest management practice of either thinning or clearcutting.
Forest_Production_Costs_2021$_per_bdt: The total production costs of forestland biomass per bone dry ton (bdt) in 2021 dollars.
Timber_Costs_2014$_per_dry_ton: The costs for timber per dry ton biomass in 2014 dollars.
Chipper_Costs_2014$_per_dry_ton: The costs for chipper per dry ton biomass in 2014 dollars.
Total_Costs_2014$_per_dry_ton: The costs for timber and chipper per dry ton biomass in 2014 dollars.
Cost_by_FHS.csv is a comma-separated file that holds the following variables:
Forest_Harvest_Systems: This is for different forest silvicultural and harvest methods.
Cut: This indicates the forest management practice of either thinning or clearcutting.
Harverst_Production_Costs_2021$_per_bdt: The harvest costs of production per bone dry ton (bdt) in 2021 dollars.
Chipping_Costs_2021$_per_bdt: The costs of production to chip the biomass per bone dry ton (bdt) in 2021 dollars.
Total_Costs_2021$_per_bdt: The costs of production to harvest and chip the biomass per bone dry ton (bdt) in 2021 dollars.
Harverst_Production_Costs_2014$_per_bdt: The harvest costs of production per bone dry ton (bdt) in 2014 dollars.
Chipping_Costs_2014$_per_bdt: The costs of production to chip the biomass per bone dry ton (bdt) in 2014 dollars.
Total_Costs_2014$_per_bdt: The costs of production to harvest and chip the biomass per bone dry ton (bdt) in 2014 dollars.
This dataset provides additional variables for modelers and other interested stakeholders for yield assumptions for modeled energy crops on agricultural land in the CONUS, as modeled by the POLYSYS model.
The yield unit was changed from lb/ac to dt/ac post-processing.
V0.1 changes include: tillage for subclass like 'energy crop' is now '[null]' and for subclass = 'Intermediate oilseeds' 'till' is now 'CT' (Conventional Tillage), format now uses pipe (|) delimiter.
This dataset can be cited as:
Hellwinckel, C., D. de la Torre Ugarte, H. Cook, M Davis, M. Langholtz. 2024. “A Customized Dataset for Yield from Agricultural Resources Modeled with POLYSYS as highlighted in Chapter 5: Biomass from Agriculture.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2350581.
The chapter relevant to this research can be cited as:
Hellwinckel, C., D. de la Torre Ugarte, J. L. Field, and M. Langholtz. 2024. “Chapter 5: Biomass from Agriculture.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316171.
Date: 20240523
State: Published
Title: A Customized Dataset for Yield from Agricultural Resources Modeled with POLYSYS as highlighted in Chapter 5: Biomass from Agriculture
Authors: Chad Hellwinckel , Daniel DeLaTorre Ugarte , Hope Cook, Maggie Davis , Matthew H Langholtz
Last_editor: Maggie R. Davis
Short_Description: This dataset provides additional variables for modelers and other interested stakeholders for yield assumptions for modeled energy crops on agricultural land in the CONUS, as modeled by the POLYSYS model.
Description: We present an updated estimate of potential biomass supplies from agricultural lands. The potential for farmers to respond to new markets for biomass has been assessed with the Policy Analysis System Model (POLYSYS) in previous versions of the billion-ton report (DOE 2017, 2016, 2011) and other studies (Oyedeji et al. 2021; Davis et al. 2020; Langholtz et al. 2019; Woodbury et al. 2018; Eaton, Langholtz, and Davis 2018; Langholtz et al. 2014; Langholtz et al. 2012; Jensen et al. 2007; De la Torre Ugarte and Ray 2000; Hellwinckel et al. 2015). Building on previous analyses, POLYSYS was used to update estimates of biomass supplies and prices from agricultural lands given environmental, land use, and technical constraints. The POLYSYS model, methods, and constraints are summarized in the chapter and detailed in the appendix. Changes from previous billion-ton reports include the use of the new 2023 USDA baseline, reporting of mature-market biomass supplies (see chapter Section 5.2: Methods Summary), oilseed supply estimates, and reporting of changes to carbon emissions and soil sequestration. For more information, please see https://bioenergykdf.ornl.gov/document/customized-dataset-yield-agricultural-resources-modeled-polysys
Keywords: Feedstock Production
Variables: Class,subclass,resource,fips,county,state_name,usdaregion,sqmi,till,model_name,scenario_name,prc_ofr,prod,harvest,yield_at_harvest,mean_annual_yield_at_harvest,produnit,yldunit,btu_ton,production_energy_content,energy_content_unit,prodens_dtpersqmi,landsrce,year,rotation
Variable descriptions:
Class is a assigned label to denote land source or analysis source corresponding to the agricultural chapter of 2023 Billion-Ton Report,subclass defines a class of biomass crops, resource shows the common name of the biomass crop type, fips is a geographic defined variable corresponding to counties provided as a 5-digit code, county as county name, state_name, usdaregion is a US Department of Agriculture defined region, sqmi is square miles for the county, till shows tillage class as nt = no till & rt = reduced tillage & ct = conventional tillage, model_name defines source of data from the POLYSYS model, scenario_name defined BT23 specific scenarios (see report for more information) as mm = mature market and low to high scenarios as well as an emerging scenario, prc_ofr as price offered from the POLYSYS model, prod as production with corresponding units, harvest as harvested acres corresponding to production levels, yield_at_harvest as the yield with corresponding units at the time of harvest, mean_annual_yield_at_harvest as the mean annual yield at this time of harvest with defined units, produnit as production units corresponding to “prod” column, yldunit as yield unit corresponding to yield_at_harvest and mean_annual_yield_at_harvest, btu_ton as British Thermal Unit (btu) per ton of resource, production_energy_content as calculated from btu_ton and prod, energy_content_unit as the units corresponding to production_energy_content, prodens_dtpersqmi as production density in dry tons per square mile as calculated from semi and prod, landsrce as the type of agricultural land defined at onset of simulation, year as year of harvest (selected) to correspond to scenario year, rotation as the defined rotation of relevant resources (oilseeds) modeled in POLYSYS
Publisher: Bioenergy Knowledge Discovery Framework (bioenergyKDF) Data Center
Publication Date: 2024-05-23
Type: plain text file (txt) with pipe (|) delimiter, as zipped (compressed)
Identifier: version 0.1 (v0.1)
DOI: https://doi.org/10.23720/BT2023/2282885
Relation: 2023 Billion-Ton Report, resource=“10.23720/BT2023/2282885” relation=“isVersionOf”
Description: Version 0.1: Corrected error within version 0.0 by changing data within variable ‘till’ to ‘[null]’ for ‘subclass’ of ‘Energy crops, herbaceous’ and ‘Energy crops, woody’ and ‘till’ is CT (Conventional Tillage) for ‘subclass’ of ‘Intermediate Oilseeds’
Source: agricultural_based_biomass.AgriResdJetCropProd_wHrvYldwAnnl
Language: English
Rights: CC0-1.0 license
Please cite as:
Coleman, A., K. Davis, J. DeAngelo, T. Saltiel, B. Saenz, L. Miller, K. Champion, E. Harrison, and A. Otwel. 2024, Data from Emerging Resources: CO2 of Chapter 7.3 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (KDF) Data Center, https://doi.org/10.23720/BT2023/2319081
Stationary sources of CO2 arise from a wide range of industrial and commercial activities, and their characteristics can vary between facilities in terms of CO2 purity, the type and percentage of any trace contaminants, and the temperature and pressure of emissions (EPA 2022a). Based on EPA’s Greenhouse Gas Reporting Program (GHGRP) data, it is estimated that 2,724 million tons of CO2 were emitted by stationary sources in 2022 (EPA 2022b). About 95% (2,584 million tons) comes from non-biogenic sources, and the remaining 5% (141 million tons) is from biogenic sources. This dataset provides High Purity data and Total Supply through the Download Tool
This dataset is authorized for use under CC0-1.0 license, provided the dataset is cited as follows:
Coleman, A., K. Davis, J. DeAngelo, T. Saltiel, B. Saenz, L. Miller, K. Champion, E. Harrison, and A. Otwel. 2024, Data from Emerging Resources: CO2 of Chapter 7.3 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (KDF) Data Center, https://doi.org/10.23720/BT2023/2319081
Text from the associated chapter of the 2023 Billion-Ton Report should be cited as:
Chapter 7.3 — Badgett, A., G. Cooney, J. Hoffmann, and A. Milbrandt. 2024. “Chapter 7.3: CO2 Emissions from Stationary Sources.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316177.
What’s in the 2023 Billion-Ton Report?Oak Ridge National Laboratory’s Matthew Langholtz provides a short background summary for each of the resource classes and market scenarios explored in the 2023 Billion-Ton Report.
BioenergyKDF: How to access the dataOak Ridge National Laboratory’s Maggie Davis shares how to access the resources from the 2023 Billion-Ton Report using the Bioenergy KDF data portal.
2023 Billion-Ton Report: MacroalgaeAnne Otwell, a contractor in the Department of Energy's Bioenergy Technologies Office, highlights the findings about macroalgae from the 2023 Billion-Ton Report.
The U.S. Department of Energy Bioenergy Technology Office's (BETO's) 2023 Billion-Ton Report (BT23) is an assessment of renewable carbon resources potentially available in the United States. BT23 explores these resources in terms of quantity, price, geographical density and distribution, and market maturity. Resource quantities in this report are limited by specified economic and environmental sustainability constraints. Good practices are needed to ensure biomass production has positive environmental outcomes.
BT23 supports BETO's mission, particularly the 2023 Multi-Year Program Plan. To access 2023 Billion-Ton Report PDFs, appendices, and high-level messages, navigate to the 2023 Billion-Ton Report landing page at https://energy.gov/eere/bioenergy/2023-billion-ton-report-assessment-us… on the U.S. Department of Energy Bioenergy Technologies Office website.
Please cite the 2023 Billion-Ton Report as: U.S. Department of Energy. 2024. 2023 Billion‐Ton Report: An Assessment of U.S. Renewable Carbon Resources. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. ORNL/SPR-2024/3103. doi: 10.23720/BT2023/2316165.
a. Cotton gin trash and rice hulls, totaling 2.1 and 1.3 million tons per year in all scenarios, were omitted in error from the BT23 figures and initial data release. Data for these resources were added to the BT23 Agricultural Download on April 23rd, 2024. Methods are described in BT23 Appendix C, pages 6-7.
b. Orchard prunings, totaling 6 million tons per year in all scenarios, were classified as agricultural processing waste in the report, but are now classified as agricultural residues in the data. The combined changes from cotton gin trash, rice hulls, and orchard prunings cause agricultural residues to increase by 6 million tons per year and agricultural processing wastes to decrease by 2.5 million tons per year, as compared to values provided in Summary Table ES-1. This is less than a 1% change in the national results in all scenarios.
Constraints
Biomass resources in the 2023 Billion-Ton Report are presented as production capacity under specified environmental constraints, prices, and market scenarios. Modeling varies by resource class. For example:
Agricultural residue production capacity is limited to about 1/3 of national total by retention constraints for soil conservation.
Timberland resources are constrained such that total harvests are less than net growth, and sensitive areas are excluded.
Energy crop production capacity is modeled as producer response to biomass markets in addition to projected demands for food, feed, fiber, and export. More detail is provided this summary document and in the report.
Errata
Data for rice hulls and cotton gin trash are missing in the report, but have been added in the data portal (see data update information in “Featured Data Updates”)
In Table 1.5 on page 15, the phrase “except where cable systems are in use (Northwest United States)” is an error. Cable harvesting systems were modeled for conventional timber products, but biomass from logging residues from cable harvesting systems were not included in the analysis. This assumption to exclude logging residues from cable harvesting systems can be questioned, because cable harvesting systems produce piles of logging residues at collection.
In Figure ES-1 on page xix, labeling of microalgae and macroalgae in the top right of the figure are switched. The correct labeling should follow the symbology provided in the lower right of the figure, i.e. 169 million tons per year of microalgae at a weighted average price of $650 per ton, and 79 million tons per year of macroalgae at $500 per ton.
In Figure ES-6 on page xxviii, under “Remaining timberland (unharvested)”, the "/year" was included in error. This is because remaining timberland is a stock, not an annual rate of production. However, the “/year” is correct for the “Harvest for conventional forest products” and “Reference scenario (small-diameter trees)” categories in the same figure.
In Figure 5.11 on page 116, the primary and secondary y-axis scales are misaligned. The axes values should align with the horizontal lines.
In the text box on page 23, “During CO2 fermentation some of this recycled CO2 can be harnessed…” should instead say “During fermentation some of this recycled CO2 can be harnessed…”
The following errors in v0.1 of waste data were corrected in v1.0:
The wet waste and solid waste price data released were erroneously inflated 14%, and has been reduced to 0 to report as 2022$. The wastes summary in Table 3.1 remains unchanged as $2022.
A moisture content of 6% was assumed for waste paper, which was corrected to 5.5%, causing an increase of 447,000 dry tons of waste paper (i.e, 0.5% of waste paper).
Langholtz, Matthew H., Davis, Maggie, Hellwinckel, Chad, De La Torre Ugarte, Daniel, Efroymson, Rebecca, Jacobson, Ryan, Milbrandt, Anelia, Coleman, Andre, Davis, Ryan, Kline, Keith L., Badgett, Alex, Curran, Scott, Schmidt, Erik, Theiss, Timothy, Fried, Jeremy, English, Burton, Lambert, Lixia, Cook, Hope, Field, John, Abt, Robert, Parish, Esther, Rossi, David, Abt, Karen, Brandt, Craig, Saltiel, Troy, Davis, Kristen, Otwell, Anne, Clark, Robin, Miller, Lee, Brandeis, Consuelo, Oyedeji, Oluwafemi, Klein, Bruno, Wiatrowski, Matthew R., Hawkins, Troy, Ou, Longwen, Singh, Udayan, Zhang, Jingyi, Gao, Song, Snowden-Swan, Lesley, Valdez, Peter, Xu, Yiling, Zhu, Yunhua, De angelo, Julianne, Nepal, Prakash, Prestemon, Jeffery, Champion, Kathleen, Saenz, Benjamin, Harrison, Eliza, O dea, Claire, Cooney, Gregory, Hoffmann, Jeffrey, Shell, Michael, and Walker, Lee. 2023 Billion-Ton Report: An Assessment of U.S. Renewable Carbon Resources. United States: N. p., 2024. Web. doi:10.2172/2441098.
[Equivalent to: U.S. Department of Energy. 2024. 2023 Billion‐Ton Report: An Assessment of U.S. Renewable Carbon Resources. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. ORNL/SPR-2024/3103. doi: 10.23720/BT2023/2316165.]
Matthew H Langholtz , Maggie Davis , Chad Hellwinckel , Daniel DeLaTorre Ugarte , Rebecca Efroymson , Ryan Jacobson , Anelia Milbrandt , Andre Coleman , Ryan Davis , Keith L. Kline , et al.
Please cite as:
A. Coleman. 2024, Data from Emerging Resources: Macroalgae of Chapter 7.2 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (KDF) Data Center, https://doi.org/10.23720/BT2023/2282995
This study represents the first U.S. full exclusive economic zone (EEZ) analysis for macroalgae biomass potential, inclusive of a marine area screening analysis, macroalgae biomass growth model, and associated TEA with harvest and farm gate biomass delivery.
This dataset is authorized for use under CC0-1.0 license, provided the dataset is cited as follows:
A. Coleman. 2024, Data from Emerging Resources: Macroalgae of Chapter 7.2 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (KDF) Data Center, https://doi.org/10.23720/BT2023/2282995
Text from the associated chapter of the 2023 Billion-Ton Report should be cited as:
Chapter 7.2 — Coleman, A., K. Davis, J. DeAngelo, T. Saltiel, B. Saenz, L. Miller, K. Champion, E. Harrison, and A. Otwell. 2024. “Chapter 7.2: Macroalgae.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316176.
This data reflects the latest analysis from the 2022 Algae Harmonization Update, which uses the latest parameterized and high-performing saline algal strain, second-generation carbon capture of point-source waste CO2 and high-pressure pipeline transport resolved to specific point-source types, saline water sourcing up to 40,000 mg/L total dissolved solids for source and makeup water salinity, blowdown water treatment and recycle, and brine disposal handling.
Description:
Land-screening sites based on culmination of data through 2020 at a minimum contiguous area of 1,000 acres
Modeled biomass growth based on lab parameterizations of Tetraselmis striata LANL 1001 saline strain (DISCOVR program) using 40-years of hourly meteorology from NLDAS-2.
Microalgae growth model coupled with a pond temperature model at 10 acres per pond running at an operating salinity of 55,000 mg/L.
Cultivation productivity targets 26 g/m2-day annual average based on nutrient-replete, high-protein biomass composition at harvest.
Microalgae harvest set at a density of 0.5 g/L to maximize productivity.
Harvested microalgae sent through three-stage dewatering via gravity settling, membrane concentration, and centrifugation to 200 g/L.
Saline groundwater used as the exclusive source of water that is accessed at depths no greater than 500 m
Blowdown water disposal via forward osmosis brine concentration and well injection; clarified water from forward osmosis is recycled internally within the algae farm.
CO2 capture and transport uses 2020 viable point-sources and reported annual mass, with capture costs and energy demands varying by CO2 concentration in the point source; CO2 utilization efficiency of the microalgae is set at 75%
Urea and diammonium phosphate are used as nitrogen/phosphorus nutrients and are based on biomass productivity and composition
This dataset is authorized for use under CC0-1.0 license, provided the dataset is cited as follows:
A. Coleman, Davis, R., B. Klein. 2024, Data from Emerging Resources: Microalgae of Chapter 7.1 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (KDF) Data Center, https://doi.org/10.23720/BT2023/2282994
Text from the associated chapter of the 2023 Billion-Ton Report should be cited as:
Davis, R., A. Coleman, T. R. Hawkins, B. Klein, J. Zhang, Y. Zhu, S. Gao, et al. 2024. “Chapter 7.1: Microalgae.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316175.
Please cite as:
Milbrandt, A., and A. Badgett. 2024, Data from Biomass from waste streams, of Chapter 3 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (bioenergyKDF)Data Center, https://doi.org/10.23720/BT2023/2282886
This dataset is authorized for use under CC0-1.0 license, provided the dataset is cited as follows:
Milbrandt, A., and A. Badgett. 2024, Data from Biomass from waste streams, of Chapter 3 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (bioenergyKDF) Data Center, https://doi.org/10.23720/BT2023/2282886
Text from the associated chapter of the 2023 Billion-Ton Report should be cited as:
Milbrandt, A., and A. Badgett. 2024. “Chapter 3: Waste Resources and Byproducts.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316168.
Additional Details include:
Wet Waste Generation
Current/near-term total wet waste resource supply is based on previous work considering modeled and per-capita-derived estimates in 2017, except yellow and brown grease, which were updated to 2019 for this report. Future total supply for sludge, food waste, and FOG is based on generation increase approximated from percent change for population from 2017 to 2050. Future total supply for animal manure is based on generation increase approximated from percent change for each manure type from 2021 to 2032. This data represents animal manure information for 23 states only due to lack of geospatial data for the remaining states. However, these 23 states represent the majority of manure production: 94%, 76%, and 93% of confined cattle, dairy, and swine, respectively. More information about the data development and sources is available in the Methods section of the Billion-Ton Study.
Wet Waste Prices
Data in 2022 dollars. A price ceiling and price floor are defined, where modeled prices that are lower than the price floor or higher than the price ceiling are assigned prices equal to the respective floor and ceiling values. This dataset assumes a price floor of $0.0/wet ton and ceiling of $100/wet ton across all scenarios.
Field description for food waste:
Price model – Field uses default value estimated by food waste cost models
Price ceiling – Field uses maximum price ceiling of $100/t. Food waste cost model estimates high food waste diversion and sorting costs, which are typical of regions with low food waste generation.
Price floor – Field uses maximum price ceiling of $0/t. Food waste cost model estimates that food waste could be available at low to zero cost due to low estimated costs of diversion and sorting, along with high local landfill tipping fees.
Field description for sludge:
No sludge resource – No sludge resource estimated within the county and no price is assigned.
Hauling approximation – County generates an extremely small amount of sludge and cost model indicates that it may be more economically viable to haul sludge to a centralized facility.
Supply curve model – Field uses default value estimated by sludge cost models
More information about the data development and sources is available in the Methods section of the Billion-Ton Study.
Solid Waste Generation
This dataset includes available (landfilled) plastic and paper/cardboard waste in 2019 by county. It doesn’t include total generation potential due to lack of geospatial data at the county level. The dataset also includes total generation for clean urban wood (MSW and C&D), rubber/leather, textile, and yard trimmings by county using the latest EPA nationwide waste generation reporting (2018) and county population that year. Future total supply for all solid waste resources is based on generation increase approximated from percent change for population from 2018/2019 to 2050 at the county level. More information about the data development and sources is available in the Methods section of the Billion-Ton Study.
Solid Waste Prices
Data in 2022 dollars. Plastic and paper/cardboard waste prices for near-term and mature-market low/medium scenarios represent the reported 3-year average (2019-2021). For the remaining materials where market prices are not readily available, market prices are estimated by considering local landfill tipping fee and added costs for material separation. An additional price adder is considered for all solid waste resources under all mature-market scenarios reflecting an estimated increase in demand. More information about the data development and sources is available in the Methods section of the Billion-Ton Study.
Landfill gas included in this dataset was sourced from: https://www.epa.gov/system/files/documents/2023-07/lmopdata.xlsx
For the latest landfill gas data, please access https://www.epa.gov/lmop/lmop-landfill-and-project-database
Please cite as:
Hellwinckel, C., D. de la Torre Ugarte, J. L. Field, and M. Langholtz. 2024, Data from Biomass from the Agricultural Land Base, of Chapter 5 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (bioenergyKDF) Data Center, https://doi.org/10.23720/BT2023/2282885
We present an updated estimate of potential biomass supplies from agricultural lands. The potential for farmers to respond to new markets for biomass has been assessed with the Policy Analysis System Model (POLYSYS) in previous versions of the billion-ton report (DOE 2017, 2016, 2011) and other studies (Oyedeji et al. 2021; Davis et al. 2020; Langholtz et al. 2019; Woodbury et al. 2018; Eaton, Langholtz, and Davis 2018; Langholtz et al. 2014; Langholtz et al. 2012; Jensen et al. 2007; De la Torre Ugarte and Ray 2000; Hellwinckel et al. 2015). Building on previous analyses, POLYSYS was used to update estimates of biomass supplies and prices from agricultural lands given environmental, land use, and technical constraints. The POLYSYS model, methods, and constraints are summarized in the chapter and detailed in the appendix. Changes from previous billion-ton reports include the use of the new 2023 USDA baseline, reporting of mature-market biomass supplies (see chapter Section 5.2: Methods Summary), oilseed supply estimates, and reporting of changes to carbon emissions and soil sequestration.
Note: Oilseed Crops have rotations and therefore may have duplicate rows by resource with differing production units. Users may sum these production numbers for aggregated data.
Note 2: Cotton gin trash and Rice hulls are downloaded separately and were not included in visualizations by resource.
This dataset is authorized for use under CC0-1.0 license, provided the dataset is cited as follows:
Hellwinckel, C., D. de la Torre Ugarte, J. L. Field, and M. Langholtz. 2024, Data from Biomass from the Agricultural Land Base, of Chapter 5 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (bioenergyKDF) Data Center, https://doi.org/10.23720/BT2023/2282885
Text from the associated chapter of the 2023 Billion-Ton Report should be cited as:
Hellwinckel, C., D. de la Torre Ugarte, J. L. Field, and M. Langholtz. 2024. “Chapter 5: Biomass from Agriculture.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316171.
